Efficient encoding and decoding sequences using variational autoencoders

Number: 11238341
Published: 2022-02-01
Filed: 2018-06-29
Assignee: Disney Enterprises, Inc.
Inventors: Mandt; Stephan Marcel, Li; Yingzhen
CPC: G06N3/0455; H03M7/30; H03M7/00; G06N3/045; G06N3/0475; G06N3/0499; H03M13/6502; H03M1/00; G06N3/08; G10L19/04; G06N3/047; G06N3/09
Verdict: Set aside generic sequence compression/codec ML
Source: Google Patents · FreePatentsOnline

Abstract

Embodiments include applying neural network technologies to encoding/decoding technologies by training and encoder model and a decoder model using a neural network. Neural network training is used to tune a neural network parameter for the encoder model and a neural network parameter for the decoder model that approximates an objective function. The common objective function may specify a minimized reconstruction error to be achieved by the encoder model and the decoder model when reconstructing (encoding then decoding) training data. The common objective function also specifies for the encoder and decoder models, a variable f representing static aspects of the training data and a set of variables z1:T representing dynamic aspects of the training data. During runtime, the trained encoder and decoder models are implemented by encoder and decoder machines to encode and decoder runtime sequences having a higher compression rate and a lower reconstruction error than in prior approaches.

Background

BACKGROUND Field of the Various Embodiments (1) Embodiments of the present invention generally relate to computer science and, more specifically, to efficient encoding and decoding sequences using variational autoencoders. DESCRIPTION OF THE RELATED ART (2) Codecs (COmpressor-DECompressor) are compression algorithms designed to encode/compress and decode/decompress source data (input sequences) to reduce the size of the source data. Many popular codecs produce lossy compression that reduces the data size of the source data by removing unnecessary or less important information of the source data. Such lossy codecs encode source data using inexact approximations and the partial discarding of the source data, but allows for a large reduction in data size when compared to uncompressed data or lossless compression. Lossy compression is commonly used to encode multimedia data sequences, such as audio or video, in a wide range of applications, such as playback from local storage and remote streaming over a network. Compression is highly useful as it reduces the data size of the source data, which significantly reduces the storage resources required to store the source data. Also, if the source data is to be transmitted over a network, compression allows for faster transmission of the source data and/or reduces the bit-rate and bandwidth requirements for transmitting the source data. A decoder algorithm is then used to decode/decompress the encoded/compressed data to reverse the enco

Claims

1. A computer-implemented method for decoding an encoded sequence, the method comprising: receiving the encoded sequence at a decoder engine; and decoding the encoded sequence via a decoder model implemented by the decoder engine to generate a decoded sequence, wherein the decoder model is trained through neural network training to decode static aspects of the encoded sequence that are substantially consistent throughout an entirety of the encoded sequence. || 10. A non-transitory computer-readable medium storing program instructions that, when executed by a processor, cause the processor to decode an encoded sequence by performing the steps of: receiving the encoded sequence at a decoder engine; and decoding the encoded sequence via a decoder model implemented by the decoder engine to generate a decoded sequence, wherein the decoder model is trained through neural network training to decode static aspects of the encoded sequence that are substantially consistent throughout an entirety of the encoded sequence. || 19. A computing system configured for decoding an encoded sequence, comprising: a memory that includes a decoder engine; and a processor that is coupled to the memory and, upon executing the decoder engine, performs the steps of: receiving the encoded sequence; and decoding the encoded sequence via a decoder model implemented by the decoder engine to generate a decoded sequence, wherein the decoder model is trained through neural network training to decode static aspects of the encoded sequence that are substantially consistent throughout an entirety of the encoded sequence.